193 results about "Context model" patented technology

The invention discloses a focused mode context retrieval enhancement method and system in a knowledge retrieval system, and the method comprises the steps: carrying out the format conversion and preprocessing of a knowledge document, segmenting a text through a focused overlapping partitioning algorithm, and extracting structural features through a natural language processing technology to generate a directory; the text blocks are converted into semantic vectors through a BERT-Context model, and the semantic vectors are stored in a vector library; during retrieval, text blocks are quickly positioned according to question similarity, a long context is formed through splicing, similarity ranking is calculated, cue words are generated by high-correlation text blocks and questions together, and answers are obtained by inputting the cue words into a generation model. According to the method, the problems of semantic deficiency, incomplete context information and undetailed user retrieval context in the existing knowledge retrieval system can be effectively solved, the accuracy and integrity of knowledge retrieval are improved, and particularly, the method is excellent in performance when processing problems in complex fields.

The invention relates to the field of intelligent interaction, and discloses an intelligent hardware dynamic interaction system based on voice semantic fusion and multi-modal perception, which comprises the following steps of: constructing a context model of continuous operation by collecting continuous voice instructions, gesture actions and expression information of a user; semantic analysis and feature fusion are carried out on currently collected voice, gesture and expression features, meanwhile, credibility indexes of all modes are calculated through a weighting or deep learning model, weighting correction is carried out on a fusion result, a real-time feedback algorithm is adopted for weight adjustment for continuous optimization, the next operation intention of a user is predicted through deep learning, and the user experience is improved. And in combination with historical interaction data, online feedback and prediction errors, context management, modal weight and intention prediction strategies are adaptively optimized, and the updated strategies are used for next-round context acquisition and multi-modal fusion. The method has the advantage of improving the recognition accuracy in the continuous interaction scene.

The invention relates to the technical field of image compression, in particular to a hierarchical multi-context space adaptive image compression method. The method comprises the steps of obtaining a training set and a test set; constructing a spatial adaptive feature modulation network, wherein the spatial adaptive feature modulation network comprises an encoder, a decoder, a super-prior encoder and a super-prior decoder; constructing a multi-context joint entropy estimation model, wherein the multi-context joint entropy estimation model comprises a local context model, a channel context model, an in-layer anchor point context model and a cross-layer global context model; obtaining an image compression network in combination with the spatial adaptive feature modulation network and the multi-context joint entropy estimation model; respectively training and testing an image compression network by using the training set and the test set to obtain a trained image compression network; and inputting a to-be-processed image into the trained image compression network to obtain a reconstructed image. According to the invention, the image compression effect is improved.

A video decoding method includes obtaining a to-be-entropy-decoded syntax element in a current block by parsing a received bitstream, where the to-be-entropy-decoded syntax element includes a syntax element 1 or a syntax element 2 in the current block, obtaining a context model corresponding to the to-be-entropy-decoded syntax element, where both of a context model corresponding to the syntax element 1 and a context model corresponding to the syntax element 2 are determined from the same preset context model set, entropy decoding the to-be-entropy-decoded syntax element based on the context model corresponding to the to-be-entropy-decoded syntax element, and obtaining a reconstructed image of the current block based on the syntax element obtained by entropy decoding.

The invention relates to a large language model dynamic routing method and device based on context learning model characterization and a readable storage medium. Query is embedded and mapped to a language model input space by using a projection model, semantic alignment is realized, a representative evaluation set covering multi-dimensional capability is automatically screened from a benchmark question bank, and the representative evaluation set is used for evaluating the multi-dimensional capability. Performance characteristics of the model on an evaluation set are efficiently obtained at a time, and high-quality context model capability representation is formed; then real-time query embedding and context model capability representation are combined, a lightweight routing language model is used for supervised learning, so that the fine-grained model distinguishing capability is achieved, an increment embedding updating mechanism is designed, and when a new model is accessed or an old model is upgraded, cold start can be rapidly completed only through a very small number of fixed questions, so that the efficiency is improved. The calculation and maintenance cost is greatly reduced, and the accuracy, real-time performance and flexible expansibility of model routing are effectively improved.

A data platform monitors a compute environment by performing multi-stage heuristic analysis of event data representing a plurality of events occurring within the environment. The platform utilizes multiple event analyzers, each configured according to a distinct analysis heuristic, to evaluate different subsets of the event data and generate corresponding output signals. A higher-level event analyzer applies a further heuristic to the multiple output signals to generate a composite alert signal, indicating whether the combination of analyzed events collectively represents a security intrusion or other anomalous condition of sufficient severity to warrant alerting. Based on the composite alert signal, the platform performs an alert-based operation, such as generating a user-facing alert, initiating an automated mitigation, or updating a contextual model of system behavior. By combining the analytical outputs of heterogeneous heuristics, the disclosed architecture enhances the accuracy and contextual relevance of automated intrusion detection within complex computing environments.

The invention discloses an enterprise data analysis system based on natural language interaction, and relates to the field of enterprise data analysis, the system comprises seven core function modules: a dialogue management module supports a user to create a new dialogue and automatically jumps to an initial page; the report interaction module realizes query support and switching functions of a real-time report and a T + 1 report through a floating button; the file import module allows uploading of Word, PDF and JPG format files to supplement question and answer contexts; the model configuration module supports dynamic switching of different AI service interfaces; the interaction auxiliary module provides a quick question button and a dialogue interruption function; the report preview module integrates Excel and PDF plug-ins to realize detail data preview; the session sharing module generates a sharing link and sets an access permission rule; dynamic model configuration optimizes response speed and accuracy; the operation process is simplified by a suspension button and a quick questioning function; the session sharing mechanism supports cross-team cooperation; and the real-time collaborative design with the T + 1 report meets the multi-dimensional data analysis requirement.

The invention provides an edge multi-mode grading detection elder falling early warning method comprising the following steps: deploying an edge computing network comprising a master control node and a data node, and loading a context model according to a position; binding the face information of the user with the RFID tag through an identity confirmation camera; the data node triggers a millimeter wave radar to collect point cloud data through a human body infrared sensor; performing contour change rate primary detection, context model deep learning secondary detection and voice module state confirmation tertiary response; and after the tumble is confirmed, the master control node gives an early warning to the emergency center. The invention relates to the technical field of elderly monitoring.

The invention discloses a short video promotion optimization method and system, relates to the technical field of short video promotion optimization, and aims to solve the problems that when an existing short video promotion method is used for processing subtle elements which may cause different understandings of users in video contents, intentions of merchants are difficult to accurately convey, target users are difficult to reach, and the user experience is poor. The method comprises the steps of obtaining content data of a to-be-promoted video, performing multi-meaning element identification on the content data to determine at least one potential multi-meaning element in the to-be-promoted video, and determining a situational misreading risk assessment result of the potential multi-meaning element according to the potential multi-meaning element and a preset user situation model, and according to the situational misreading risk assessment result, a pre-stored promotion constraint condition and a content modification cost model, making a decision between the content modification strategy and the audience screening strategy to generate an initial intervention strategy, executing the initial intervention strategy, and optimizing the promotion process based on user feedback data obtained after execution.

This disclosure describes a lightweight image query system that utilizes multiple lightweight models to quickly and efficiently generate responses to multimodal input queries. For example, in response to a multimodal input query, the lightweight image query system utilizes multiple lightweight context models to first obtain different types of context information based on the different input modes. The lightweight image query system then utilizes a lightweight large generative model (LGM) to quickly generate a query response using the different types of context information. By using lightweight models, including multiple lightweight context models and a lightweight LGM, the lightweight image query system can efficiently provide query responses to multimodal input queries in about half the time it takes conventional systems to return a query response.

The embodiment of the invention provides an end-to-end image compression method and system based on window local attention and generalized chessboard space channel context, and belongs to the technical field of image processing. The method comprises the following steps: constructing a transformation network based on an attention module and a stacked residual block; the transformation network based on the attention module and the stacked residual block is used for executing adaptive transformation of contents through dynamic representation and neighborhood information embedding to obtain potential features; establishing a generalized chessboard space channel context model; the generalized chessboard space channel context model is used for carrying out entropy coding on the potential features; and obtaining image compression data according to the transformation network based on the attention module and the stacked residual block and the generalized chessboard space channel context model. According to the method, redundancy can be eliminated to the maximum extent, excellent rate distortion performance is achieved, and meanwhile high-throughput parallel computing efficiency is ensured.

A method of video encoding includes performing context modeling to determine a context model for each of a number of bins of syntax elements corresponding to residues of a region of a transform skipped block in a current picture. The number of the bins of syntax elements being context coded does not exceed a maximum number of context coded bins set for the region. The method further includes encoding, according to Block Differential Pulse Code Modulation (BDPCM), the syntax elements based on the determined context models. When the maximum number of context coded bins is reached, remaining bins of syntax elements are encoded based on a bypass model.

The invention discloses a BIM-based construction resource real-time management method and system, and relates to the technical field of building construction. The method comprises the steps of obtaining real-time Internet of Things data flow and BIM model data of a construction site, dynamically associating physical resources in the Internet of Things data flow to a task component of a BIM model, and generating contextual consistency of fused sensing data and a current task; constructing a dynamic context model; inputting the current state of the dynamic context model into a preset construction knowledge graph, identifying an abnormal event, performing causal path tracing on the abnormal event, and outputting an abnormality diagnosis report at least including diagnosis confidence; and pushing the abnormality diagnosis report to a manager, receiving feedback data, and optimizing the inference rule of the construction knowledge graph by using the feedback data and the abnormality diagnosis report. According to the invention, the accuracy and adaptability of intelligent decision-making of construction resource management are effectively improved.

The invention discloses a design method for an on-orbit accompanying measurement and control communication machine state machine model, and the method comprises the steps: constructing a data model of a measurement and control communication machine state machine, and enabling the data model to be used for calling data externally; constructing a context model of the state machine of the measurement and control communication machine based on the data model, and constructing a logic architecture of the state machine of the measurement and control communication machine based on scene service data; based on the logic architecture, the context model obtains an executable extended state machine and updates the feature attributes; obtaining a simulation model through periodic continuation based on the state machine capable of executing expansion and the context model, wherein the simulation model is used for processing uplink and downlink remote control and telemetering, voice and distance measurement service data of satellite communication, responding to a communication machine instruction set of the state machine of the measurement and control communication machine and outputting communication machine product telemetering; universal configuration is implemented based on the communicator instruction set and the communicator product telemetry.

The invention discloses a power grid standard semantic understanding driven question answering system. The system comprises a user question receiving and preprocessing module, a question classification module, a real-time context integration module, a question processing and strategy selection module and an answer generation and user interaction module. The problem classification module classifies the problems based on a hybrid algorithm and determines the types of the problems; the real-time context integration module monitors and analyzes power grid data, weather conditions and maintenance records in real time, and constructs a current context model; the problem processing and strategy selection module is used for selecting the most suitable processing strategy according to the problem type and the real-time context; and the answer generation and user interaction module generates a natural language answer and displays the natural language answer to the user. By combining ChatGLM, CRF and prompt technologies, the system can efficiently and accurately process various power grid related problems and improve question and answer performance and user experience, is suitable for multiple fields such as power grid operation, maintenance and management, and has wide application prospects and remarkable practical value.

The invention relates to the technical field of automatic driving, in particular to an open type automatic driving safety management system and method supporting multi-benefit-party strategy arbitration, and the system comprises an open type safety strategy interface module which is used for defining a standardized strategy description specification; the strategy plug-in management module is used for receiving and loading the strategy plug-ins conforming to the strategy description specifications; the perception and fusion module is used for collecting the vehicle state data, the environment perception data and the service context data and fusing the data to generate a unified context model; the multi-strategy evaluation module is used for screening a candidate strategy set according to a unified context model and a trigger condition when an abnormal event is detected, and evaluating and selecting an optimal strategy; and the strategy execution and monitoring module is used for executing the action template corresponding to the optimal strategy. According to the invention, the automatic driving vehicle can select adaptive degradation strategies according to different operation scenes and business requirements, and the adaptability of the system to diversified business scenes is improved.

Various embodiments of the present disclosure relate generally to systems and methods for generating multi-view interactive digital media representations in a virtual reality environment. According to particular embodiments, a plurality of images is fused into a first content model and a first context model, both of which include multi-view interactive digital media representations of objects. Next, a virtual reality environment is generated using the first content model and the first context model. The virtual reality environment includes a first layer and a second layer. The user can navigate through and within the virtual reality environment to switch between multiple viewpoints of the content model via corresponding physical movements. The first layer includes the first content model and the second layer includes a second content model and wherein selection of the first layer provides access to the second layer with the second content model.

The embodiment of the application provides a kind of quantization parameter coding method and electronic equipment, the coding method includes: first, according to the QP reconstructed value of the QP of coded non-private CU in CU QP group, determine the QP prediction value of non-private CU to be coded in CU QP group;Then, according to the QP original value of non-private CU to be coded and the QP prediction value of non-private CU to be coded, determine the QP residual of non-private CU to be coded;Subsequently, according to the number of coded non-private CU in CU QP group, determine the context model corresponding to the QP residual of non-private CU to be coded;After that, according to the context model corresponding to the QP residual of non-private CU to be coded, the QP residual of non-private CU to be coded is entropy coded. In this way, it can be guaranteed that the decoding process of the QP of non-private CU at the decoding end and the encoding process of the QP of non-private CU at the encoding end are consistent.

The invention discloses an efficient video stream transmission and management platform based on a message queue, and relates to the technical field of video stream transmission. After receiving a transmission instruction, the platform collects and stores operation parameters of video equipment in real time to monitor whether the equipment state is healthy or not, obtains an original video stream from the accessed video equipment, and transmits the original video stream to the video equipment; the method comprises the steps of generating a comprehensive judgment index through first judgment analysis to judge a network condition, transcoding an original video stream to generate a video multi-layer resolution slice, constructing a lossless coding context model based on the video multi-layer resolution slice through second judgment analysis, generating a high sub-band primer set and a low sub-band primer set, and generating a high sub-band primer set and a low sub-band primer set. Message units with priorities are constructed to facilitate transmission and management of video streams; according to the method, the security of image transmission can be enhanced, efficient lossless compression under multiple resolutions is also realized, efficient transmission is ensured, and particularly, intelligent optimization of video transmission is realized in a scene with limited network resources.

An artificial intelligence (AI) assisted generative digital task fulfillment process within digital model platforms is provided. Disclosed are methods and systems for carrying out digital tasks through generative AI, including tasks related to the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. The method includes receiving access to a context AI model trained on Internet-scale data, receiving a user prompt indicating the digital task, and generating contextual data based on the user prompt using the context AI model, where the contextual data identifies a syntax AI model. The method includes training the syntax AI model to generate a template script having a placeholder variable for a parameter related to the digital task. The method also includes using a parameter substitution process to generate the orchestration script by substituting the variable with a parameter value.

The context-aware optimization method includes training a context model by determining whether to split each node in the context by identifying a first subset of virtual context to evaluate by identifying a second subset of virtual contexts to evaluate and obtaining an encoding cost of splitting of the context model for each virtual context in the second subset and identifying the first subset of virtual contexts to evaluate by selecting a predetermined number of virtual contexts from the second subset based on the encoding cost such that the predetermined number of virtual contexts with lowest encoding cost are selected. The modified tree-traversal method includes encoding a mask or performing a speculative-based method. The modified entropy coding method includes representing data into an array of bits, using multiple coders to process each bit in the array and combining the output from the multiple coders into a data range.

There is provided an video encoding / decoding method and apparatus. The video decoding method comprises acquiring a bitstream including a predetermined context element, performing at least one of a context model determination, a probability update, and a probability interval determination on the predetermined syntax element, and arithmetically decoding the predetermined syntax element on the basis of a result of the performance.

An image decoding method performed by a decoding device according to the present document comprises the steps of: deriving a context model for a sign flag of a current residual coefficient of a current block; and decoding the sign flag on the basis of the context model, wherein the context model for the sign flag is derived on the basis of a sign flag of a residual coefficient decoded before the current residual coefficient in the current block.

A computer-implemented system and method for building context models in real time is provided. A contextual situation of a user is determined and compared with models each associated with actions that represent a situation. A determination is made that none of the models represents the situation of the user. A similarity value is determined for each of at least a portion of the models with the situation of the user. A threshold is applied to the similarity values. All models associated with a similarity value that satisfies the threshold are selected. The selected models are merged into a new model for the situation by utilizing a weight associated with each of the selected models to identify those actions in the selected models for inclusion in the new model.

The present invention relates to an image encoding / decoding method and device. The image decoding method according to an embodiment of the present invention may comprise the steps of: acquiring a bitstream including an encoded syntax element; determining a context model for the encoded syntax element; determining a probability model on the basis of the context model; decoding the encoded syntax element on the basis of the probability model; and determining whether to update the probability model.

The invention discloses a context-based Internet of Things data pushing method, which relates to the technical field of data pushing, and comprises the following steps: collecting original data, inputting preprocessed data into a context fusion engine, fusing the preprocessed data into context data, identifying agricultural context events based on a context data application rule engine and a machine learning reasoning model, and outputting pushing event data. According to the method, the soil humidity data, the air temperature and humidity data, the illumination intensity data and the meteorological platform data are fused through the unified agricultural situation model, unified processing and situation reasoning of the multi-source data are achieved, and the accuracy and practicability of the pushed information are improved. The agricultural situation event is recognized by combining the rule engine and the machine learning reasoning model, and the pushing frequency and content are adaptively adjusted according to the pushing event data, so that the communication and calculation burden is reduced, the user experience is optimized, and a good application effect is achieved.

Methods and apparatus are provided for initializing a set of context model probability sets for slices in context-based adaptive binary arithmetic coding (CABAC). An exemplary video decoding method includes selecting a first set of probability parameters for initializing one or more context models of a B-slice from a predefined plurality of sets of probability parameters; and performing entropy decoding of the B-slice based on the one or more context models and the first set of probability parameters, where the selection is based on a coding condition of the B-slice or a signal in the bitstream.

In a dialogue system outputting utterances in a dialogue manner, an utterance filtering device for preventing output of possibly problematic expression includes: a pre-trained context model trained, in response to an input of a word vector sequence representing an utterance, to output a probability vector comprising elements indicating probability of each of the words in a prescribed word group appearing in a context in which the utterance is placed; and a determining unit 456 configured to input a word vector sequence representing a subject utterance to the context model, and to determine whether the subject utterance is to be discarded or approved depending on whether a value determined as a prescribed function of a probability vector output by the context model in response to the input is equal to or larger than a threshold value.

A video decoding method includes obtaining a to-be-entropy-decoded syntax element in a current block by parsing a received bitstream, where the to-be-entropy-decoded syntax element includes a syntax element 1 or a syntax element 2 in the current block, obtaining a context model corresponding to the to-be-entropy-decoded syntax element, where both of a context model corresponding to the syntax element 1 and a context model corresponding to the syntax element 2 are determined from the same preset context model set, entropy decoding the to-be-entropy-decoded syntax element based on the context model corresponding to the to-be-entropy-decoded syntax element, and obtaining a reconstructed image of the current block based on the syntax element obtained by entropy decoding.

There is provided an video encoding / decoding method and apparatus. The video decoding method comprises acquiring a bitstream including a predetermined context element, performing at least one of a context model determination, a probability update, and a probability interval determination on the predetermined syntax element, and arithmetically decoding the predetermined syntax element on the basis of a result of the performance.